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Excited-State Dynamics and Optically Detected Magnetic Resonance of Solid-State Spin Defects from First Principles

Published 9 Apr 2024 in cond-mat.mtrl-sci | (2404.05917v3)

Abstract: Optically detected magnetic resonance (ODMR) is an efficient and reliable method that enables initialization and readout of spin states through spin-photon interface. In general, high quantum efficiency and large spin-dependent photoluminescence contrast are desirable for reliable quantum information readout. However, reliable prediction of the ODMR contrast from first-principles requires accurate description of complex spin polarization mechanisms of spin defects. These mechanisms often include multiple radiative and nonradiative processes in particular intersystem crossing (ISC)among multiple excited electronic states. In this work we present our implementation of the first-principles ODMR contrast, by solving kinetic master equation with calculated rates from \textit{ab initio} electronic structure methods then benchmark the implementation on the case of the negatively-charged nitrogen vacancy center in diamond. We show the importance of correct description of multi-reference electronic states and pseudo Jahn-Teller effect for quantitatively, even qualitatively correct prediction of spin-orbit coupling (SOC) and the rate of ISC. We present the complete calculation of SOC for different ISC processes that align with both group theory and experimental observations. Moreover, we provide a comprehensive picture of excitation and relaxation dynamics, including previously unexplored internal conversion processes. We show good agreement between our first-principles calculations and the experimental ODMR contrast under magnetic field. We then demonstrate reliable predictions of magnetic field direction, pump power, and microwave frequency dependency, as important parameters for ODMR experiments. Our work provides a predictive computational platform for spin polarization and optical readout of solid-state quantum defects from first principles.

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